{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import numpy.random as rng\n",
    "import pandas.io.data as web\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_prices(symbol):\n",
    "    start, end = '2007-05-02', '2016-04-11'\n",
    "    data = web.DataReader(symbol, 'yahoo', start, end)\n",
    "    data=pd.DataFrame(data)\n",
    "    prices=data['Adj Close']\n",
    "    prices=prices.astype(float)\n",
    "    return prices\n",
    "\n",
    "def get_returns(prices):\n",
    "        return ((prices-prices.shift(-1))/prices)[:-1]\n",
    "    \n",
    "def get_data(list):\n",
    "    l = []\n",
    "    for symbol in list:\n",
    "        rets = get_returns(get_prices(symbol))\n",
    "        l.append(rets)\n",
    "    return np.array(l).T\n",
    "\n",
    "def sort_data(rets):\n",
    "    ins = []\n",
    "    outs = []\n",
    "    for i in range(len(rets)-100):\n",
    "        ins.append(rets[i:i+100].tolist())\n",
    "        outs.append(rets[i+100])\n",
    "    return np.array(ins), np.array(outs)\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "symbol_list = ['C', 'GS']\n",
    "rets = get_data(symbol_list)\n",
    "ins, outs = sort_data(rets)\n",
    "ins = ins.transpose([0,2,1]).reshape([-1, len(symbol_list) * 100])\n",
    "div = int(.8 * ins.shape[0])\n",
    "train_ins, train_outs = ins[:div], outs[:div]\n",
    "test_ins, test_outs = ins[div:], outs[div:]\n",
    "\n",
    "#normalize inputs\n",
    "train_ins, test_ins = train_ins/np.std(ins), test_ins/np.std(ins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "positions = tf.constant([-1,0,1]) #long, neutral or short\n",
    "num_positions = 3\n",
    "num_symbols = len(symbol_list)\n",
    "num_samples = 20\n",
    "\n",
    "n_input = num_symbols * 100\n",
    "n_hidden_1 = 10 # 1st layer number of features\n",
    "n_hidden_2 = 10 # 2nd layer number of features\n",
    "n_classes = num_positions * num_symbols # MNIST total classes (0-9 digits)\n",
    "\n",
    "\n",
    "# define placeholders \n",
    "x = tf.placeholder(tf.float32, [None, num_symbols * 100])\n",
    "y_ = tf.placeholder(tf.float32, [None,  num_symbols])\n",
    "\n",
    "weights = {\n",
    "    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n",
    "    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n",
    "    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\n",
    "}\n",
    "biases = {\n",
    "    'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
    "    'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n",
    "    'out': tf.Variable(tf.random_normal([n_classes]))\n",
    "}\n",
    "\n",
    "def multilayer_perceptron(x, weights, biases):\n",
    "    # Hidden layer with RELU activation\n",
    "    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n",
    "    layer_1 = tf.nn.relu(layer_1)\n",
    "    # Hidden layer with RELU activation\n",
    "    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n",
    "    layer_2 = tf.nn.relu(layer_2)\n",
    "    # Output layer with linear activation\n",
    "    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n",
    "    return out_layer\n",
    "\n",
    "# Construct model\n",
    "y = multilayer_perceptron(x, weights, biases)\n",
    "\n",
    "\n",
    "\n",
    "# loop through symbol, taking the columns for each symbol's bucket together\n",
    "pos = {}\n",
    "sample_n = {}\n",
    "sample_mask = {}\n",
    "symbol_returns = {}\n",
    "relevant_target_column = {}\n",
    "for i in range(num_symbols):\n",
    "    # isolate the buckets relevant to the symbol and get a softmax as well\n",
    "    symbol_probs = y[:,i*num_positions:(i+1)*num_positions]\n",
    "    symbol_probs_softmax = tf.nn.softmax(symbol_probs) # softmax[i, j] = exp(logits[i, j]) / sum(exp(logits[i]))\n",
    "    # sample probability to chose our policy's action\n",
    "    sample = tf.multinomial(tf.log(symbol_probs_softmax), num_samples)\n",
    "    for sample_iter in range(num_samples):\n",
    "        sample_n[i*num_samples + sample_iter] = sample[:,sample_iter]\n",
    "        pos[i*num_samples + sample_iter] = tf.reshape(sample_n[i*num_samples + sample_iter], [-1]) - 1\n",
    "        symbol_returns[i*num_samples + sample_iter] = tf.mul(\n",
    "                                                            tf.cast(pos[i*num_samples + sample_iter], float32), \n",
    "                                                             y_[:,i])\n",
    "        \n",
    "        sample_mask[i*num_samples + sample_iter] = tf.cast(tf.reshape(tf.one_hot(sample_n[i*num_samples + sample_iter], 3), [-1,3]), float32)\n",
    "        relevant_target_column[i*num_samples + sample_iter] = tf.reduce_sum(\n",
    "                                                    symbol_probs_softmax * sample_mask[i*num_samples + sample_iter],1)\n",
    "    \n",
    "\n",
    "\n",
    "daily_returns_by_symbol_ = tf.concat(1, [tf.reshape(t, [-1,1]) for t in symbol_returns.values()])\n",
    "daily_returns_by_symbol = tf.transpose(tf.reshape(daily_returns_by_symbol_, [-1,2,num_samples]), [0,2,1]) #[?,5,2]\n",
    "daily_returns = tf.reduce_mean(daily_returns_by_symbol, 2) # [?,5]\n",
    "\n",
    "total_return = tf.reduce_prod(daily_returns+1, 0)\n",
    "z = tf.ones_like(total_return) * -1\n",
    "total_return = tf.add(total_return, z)\n",
    "\n",
    "\n",
    "ann_vol = tf.mul(\n",
    "    tf.sqrt(tf.reduce_mean(tf.pow((daily_returns - tf.reduce_mean(daily_returns, 0)),2),0)) ,\n",
    "    np.sqrt(252)\n",
    "    )\n",
    "sharpe = tf.div(total_return, ann_vol)\n",
    "#Maybe metric slicing later\n",
    "#segment_ids = tf.ones_like(daily_returns[:,0])\n",
    "#partial_prod = tf.segment_prod(daily_returns+1, segment_ids)\n",
    "\n",
    "\n",
    "training_target_cols = tf.concat(1, [tf.reshape(t, [-1,1]) for t in relevant_target_column.values()])\n",
    "ones = tf.ones_like(training_target_cols)\n",
    "gradient_ = tf.nn.sigmoid_cross_entropy_with_logits(training_target_cols, ones)\n",
    "\n",
    "gradient = tf.transpose(tf.reshape(gradient_, [-1,2,num_samples]), [0,2,1]) #[?,5,2]\n",
    "\n",
    "#cost = tf.mul(gradient , daily_returns_by_symbol_reshaped)\n",
    "#cost = tf.mul(gradient , tf.expand_dims(daily_returns, -1))\n",
    "#cost = tf.mul(gradient , tf.expand_dims(total_return, -1))\n",
    "cost = tf.mul(gradient , tf.expand_dims(sharpe, -1))\n",
    "\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.0001).minimize(cost)\n",
    "costfn = tf.reduce_mean(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0100 cost= 0.357373 total return= 0.409767538 sharpe= 1.117977977\n",
      "Epoch: 0200 cost= 0.39784 total return= 0.456069469 sharpe= 1.244072676\n",
      "Epoch: 0300 cost= 0.462523 total return= 0.530371010 sharpe= 1.446246624\n",
      "Epoch: 0400 cost= 0.584189 total return= 0.672424912 sharpe= 1.827061296\n",
      "Epoch: 0500 cost= 0.445673 total return= 0.510583758 sharpe= 1.393794894\n",
      "Epoch: 0600 cost= 0.519553 total return= 0.596914530 sharpe= 1.624922156\n",
      "Epoch: 0700 cost= 0.565879 total return= 0.650260627 sharpe= 1.769401193\n",
      "Epoch: 0800 cost= 0.482351 total return= 0.553301036 sharpe= 1.507541776\n",
      "Epoch: 0900 cost= 0.490191 total return= 0.561884701 sharpe= 1.532655001\n",
      "Epoch: 1000 cost= 0.540358 total return= 0.619096160 sharpe= 1.688741684\n",
      "Epoch: 1100 cost= 0.641487 total return= 0.735186100 sharpe= 2.004348040\n",
      "Epoch: 1200 cost= 0.573753 total return= 0.657212317 sharpe= 1.792912483\n",
      "Epoch: 1300 cost= 0.544023 total return= 0.623561800 sharpe= 1.699504137\n",
      "Epoch: 1400 cost= 0.545976 total return= 0.625774741 sharpe= 1.705720186\n",
      "Epoch: 1500 cost= 0.54651 total return= 0.624909401 sharpe= 1.707361817\n",
      "Epoch: 1600 cost= 0.562541 total return= 0.643976569 sharpe= 1.757120132\n",
      "Epoch: 1700 cost= 0.628897 total return= 0.720324337 sharpe= 1.964194536\n",
      "Epoch: 1800 cost= 0.564138 total return= 0.645473421 sharpe= 1.762031555\n",
      "Epoch: 1900 cost= 0.580018 total return= 0.664805055 sharpe= 1.812619209\n",
      "Epoch: 2000 cost= 0.649044 total return= 0.743958056 sharpe= 2.028422356\n"
     ]
    }
   ],
   "source": [
    "# initialize variables to random values\n",
    "init = tf.initialize_all_variables()\n",
    "sess.run(init)\n",
    "# run optimizer on entire training data set many times\n",
    "train_size = train_ins.shape[0]\n",
    "for epoch in range(2000):\n",
    "    start = rng.randint(train_size-50)\n",
    "    batch_size = rng.randint(2,75)\n",
    "    end = min(train_size, start+batch_size)\n",
    "    \n",
    "    sess.run(optimizer, feed_dict={x: train_ins[start:end], y_: train_outs[start:end]})#.reshape(1,-1).T})\n",
    "    # every 1000 iterations record progress\n",
    "    if (epoch+1)%100== 0:\n",
    "        t,s, c = sess.run([ total_return, sharpe, costfn], feed_dict={x: train_ins, y_: train_outs})#.reshape(1,-1).T})\n",
    "        t = np.mean(t)\n",
    "        s = np.mean(s)\n",
    "        print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\",c, \"total return=\", \"{:.9f}\".format(t), \n",
    "             \"sharpe=\", \"{:.9f}\".format(s))\n",
    "        #print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# in sample results\n",
    "#init = tf.initialize_all_variables()\n",
    "#sess.run(init)\n",
    "d, t = sess.run([daily_returns, pos[0]], feed_dict={x: train_ins, y_: train_outs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0Fjp8dPJSOoX8f47H2/QQP1K5Eeb8yJMl0rJLSgKbDa69Ft5+MZsWyTGweDEF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\no4Vco9FoOjhayDUajaaD8/8S75fXRUkVTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1249008d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# equity curve\n",
    "for i in range(5):\n",
    "    plot(np.cumprod(d[:,[i]]+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x124ba1940>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tWmaxHS5zmw/FoBpNbOoigJxKW/mGNlUjRptc4jtmsCuAh8f2t+THSI9g8I6E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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1233b63c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#out of sample results\n",
    "d, t = sess.run([daily_returns, total_return], feed_dict={x: test_ins, y_: test_outs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jt+v45vvo2tA16Ik1e9h+7ZvnAyDl0fli8TxuyuU4PZslpaVG9Py7sl2EPWGE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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124923ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#out of sample results\n",
    "for i in range(5):\n",
    "    plot(np.cumprod(d[:,[i]]+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
